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      "cell_type": "markdown",
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        "<a href=\"https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/Donut/DocVQA/Creating_a_toy_DocVQA_dataset_for_Donut.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DNMqJ821yNVo"
      },
      "source": [
        "# Creating a toy DocVQA dataset for Donut\n",
        "\n",
        "In this notebook, we'll show how to prepare a dataset for document visual question answering (DocVQA) for Donut.\n",
        "\n",
        "The idea is to create a \"ground_truth\" column, which contains the ground truth JSON that the model needs to generate.\n",
        "\n",
        "## Set-up environment"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OqcGNPJHyOlt",
        "colab": {
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        },
        "outputId": "3d9bc179-1cd8-4830-8b96-3d80fed7bc0d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[K     |████████████████████████████████| 365 kB 12.7 MB/s \n",
            "\u001b[K     |████████████████████████████████| 1.2 MB 66.6 MB/s \n",
            "\u001b[K     |████████████████████████████████| 141 kB 88.3 MB/s \n",
            "\u001b[K     |████████████████████████████████| 212 kB 89.0 MB/s \n",
            "\u001b[K     |████████████████████████████████| 115 kB 86.3 MB/s \n",
            "\u001b[K     |████████████████████████████████| 127 kB 83.0 MB/s \n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install -q datasets"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load dataset\n",
        "\n",
        "We'll create a minimal DocVQA toy dataset. Let's first load an existing one from the [🤗 hub](https://huggingface.co/)."
      ],
      "metadata": {
        "id": "Jg5QTes7Reay"
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        "from datasets import load_dataset\n",
        "\n",
        "# load a multilingual DocVQA dataset\n",
        "dataset = load_dataset(\"olemeyer/docvqa-en-de-fr-es-it\")"
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        },
        "id": "bNHE_M5dRfE2",
        "outputId": "34a0c332-36d3-4b30-f7f1-c2f726524696"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.76k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bf0aea549635438f90675e60cef7a728"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.builder:Using custom data configuration olemeyer--docvqa-en-de-fr-es-it-7cb6a5ecdebced68\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset None/None (download: 10.68 GiB, generated: 12.49 GiB, post-processed: Unknown size, total: 23.17 GiB) to /root/.cache/huggingface/datasets/olemeyer___parquet/olemeyer--docvqa-en-de-fr-es-it-7cb6a5ecdebced68/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1feb5595aa604dd29133f1d786e7a322"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "552ef382348349d690ea5404c843bc16"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bf636fb1760b475fb757820b9a8aac7e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "797ae29cd1774eadab179f0ccb7ac98f"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "73002ebcc0694f90a6cfad5fde7eadb9"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "5635745c17f04ab1aa66f149e4e78d64"
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        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "12a968dee3504f35bb5fbb8eb03bf6ec"
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        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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        {
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          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
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        {
          "output_type": "stream",
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          "text": [
            "Dataset parquet downloaded and prepared to /root/.cache/huggingface/datasets/olemeyer___parquet/olemeyer--docvqa-en-de-fr-es-it-7cb6a5ecdebced68/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec. Subsequent calls will reuse this data.\n"
          ]
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    },
    {
      "cell_type": "markdown",
      "source": [
        "As can be seen, this dataset contains a train and test split."
      ],
      "metadata": {
        "id": "oSnFD8vcdqOQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jFqJnFnmXDOY",
        "outputId": "86fd5ab4-90c5-490c-fb7b-4319160a1fbc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['id', 'image', 'query', 'answers', 'words', 'bounding_boxes', 'answer'],\n",
              "        num_rows: 31004\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['id', 'image', 'query', 'answers', 'words', 'bounding_boxes', 'answer'],\n",
              "        num_rows: 4055\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's look at an example:"
      ],
      "metadata": {
        "id": "35xf9MIZdAzW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "example = dataset['train'][0]\n",
        "image = example['image']\n",
        "width, height = image.size\n",
        "image.resize((int(width*0.3), int(height*0.3)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 624
        },
        "id": "jlrgL0psRrcI",
        "outputId": "4450be18-ee04-48db-ecae-00fa223c2378"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=L size=508x607 at 0x7F4240E4C150>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Each document has a corresponding question, in 5 languages:"
      ],
      "metadata": {
        "id": "ym0r07o9dDlr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "example['query']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cYwFcpLvVQel",
        "outputId": "cb4e9a0c-e2c3-4883-d2cf-237964cba8c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'de': 'Wie lautet das Datum, das in diesem Brief erwähnt wird?',\n",
              " 'en': 'what is the date mentioned in this letter?',\n",
              " 'es': '¿Cuál es la fecha mencionada en esta carta?',\n",
              " 'fr': 'Quelle est la date mentionnée dans cette lettre?',\n",
              " 'it': 'Qual è la data indicata in questa lettera?'}"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's look at the corresponding answer(s):"
      ],
      "metadata": {
        "id": "tWrbU-4PdInN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "example['answers']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cxc6FLCzVnT1",
        "outputId": "349e24cf-aa89-4a1d-81bd-dc7fe35efdce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['1/8/93']"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Note that, for a DocVQA dataset, it may happen that for a given question, multiple correct answers are possible (e.g. a short or longer version of a given name). This is why there's an \"answers\" column in the dataset, rather than just an \"answer\" column."
      ],
      "metadata": {
        "id": "wHXFiFxdfpA6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Create toy dataset\n",
        "\n",
        "We first just create a small subset of the dataset loaded above. We use the [select](https://huggingface.co/docs/datasets/v2.4.0/en/package_reference/main_classes#datasets.Dataset.select) method for that purpose."
      ],
      "metadata": {
        "id": "nODSH7OpWLxe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset = dataset[\"train\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mSnsIeB4VrbT",
        "outputId": "e28d7cd2-48b8-4815-cca2-de7f92163c7a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.builder:Using custom data configuration olemeyer--docvqa-en-de-fr-es-it-7cb6a5ecdebced68\n",
            "WARNING:datasets.builder:Reusing dataset parquet (/root/.cache/huggingface/datasets/olemeyer___parquet/olemeyer--docvqa-en-de-fr-es-it-7cb6a5ecdebced68/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset = train_dataset.select(range(1000))"
      ],
      "metadata": {
        "id": "b1vixhLkW_4t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_dataset = dataset[\"test\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WsrOpOJnXBML",
        "outputId": "87b91fe4-38b9-47c2-dc48-d45162981539"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.builder:Using custom data configuration olemeyer--docvqa-en-de-fr-es-it-7cb6a5ecdebced68\n",
            "WARNING:datasets.builder:Reusing dataset parquet (/root/.cache/huggingface/datasets/olemeyer___parquet/olemeyer--docvqa-en-de-fr-es-it-7cb6a5ecdebced68/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_dataset = test_dataset.select(range(200))"
      ],
      "metadata": {
        "id": "Lz9zJBf8XFO6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import DatasetDict\n",
        "\n",
        "toy_dataset = DatasetDict(\n",
        "    {\"train\": train_dataset,\n",
        "     \"test\": test_dataset,\n",
        "})"
      ],
      "metadata": {
        "id": "nUj0JT1xXFuM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "toy_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mdoDxVbOXe4t",
        "outputId": "2bdad4ce-e45d-46ea-a490-ced3e7a65635"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['id', 'image', 'query', 'answers', 'words', 'bounding_boxes', 'answer'],\n",
              "        num_rows: 1000\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['id', 'image', 'query', 'answers', 'words', 'bounding_boxes', 'answer'],\n",
              "        num_rows: 200\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We'll push this tiny subset to the hub."
      ],
      "metadata": {
        "id": "RqJ3ssTxd-Ym"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "toy_dataset.push_to_hub(\"nielsr/docvqa_1200_examples\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236,
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        },
        "id": "IB2VTX1uXfsj",
        "outputId": "5cca964a-41b9-4a70-c4dd-ddcbcb7efda5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.dataset_dict:Pushing split train to the Hub.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Pushing dataset shards to the dataset hub:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/huggingface_hub/hf_api.py:1951: FutureWarning: `identical_ok` has no effect and is deprecated. It will be removed in 0.11.0.\n",
            "  FutureWarning,\n",
            "WARNING:datasets.dataset_dict:Pushing split test to the Hub.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ef4bd8d9dc53470c8e1605681d5e339c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Pushing dataset shards to the dataset hub:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "be1e1278e12c4fb2b65e91792c61e42a"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Add ground_truth for Donut\n",
        "\n",
        "Donut requires you to add a column with some ground truth JSON/txt/JSON lines/whatever that you'd like the model to learn to generate. Below, we define a function that we'll apply on the entire dataset to add this column.\n",
        "\n",
        "For DocVQA, the template looks like {gt_parses: [{\"question\" : {question_sentence}, \"answer\" : {answer_candidate_1}}, {\"question\" : {question_sentence}, \"answer\" : {answer_candidate_2}}, ...]}.\n",
        "\n",
        "For example, {gt_parses: [{\"question\" : \"what is the model name?\", \"answer\" : \"donut\"}, {\"question\" : \"what is the model name?\", \"answer\" : \"document understanding transformer\"}]}.\n",
        "\n",
        "The Donut authors also note: \"in case your dataset has multiple answers, `gt_parses` should be a list of dictionaries, each containing a question-answer pair.\" In case your dataset only has single answers to each question, then you can create just a `gt_parse` rather than `gt_parses`."
      ],
      "metadata": {
        "id": "d7wmSWijok4n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "toy_dataset = load_dataset(\"nielsr/docvqa_1200_examples_donut\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283,
          "referenced_widgets": [
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            "75d8b76c7fda4248ac3eea16ed6fe908",
            "a3b2d2f315804455b287e8390a928124"
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        },
        "id": "Uv_b2rT3pERX",
        "outputId": "c59f8a7a-3fd4-422a-d38c-2a93bb5ff598"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.82k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "12c2ae461136411da9d6c2e589c6348a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using custom data configuration nielsr--docvqa_1200_examples_donut-2fd6ef62bc12fd93\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset None/None (download: 140.78 MiB, generated: 429.30 MiB, post-processed: Unknown size, total: 570.08 MiB) to /root/.cache/huggingface/datasets/nielsr___parquet/nielsr--docvqa_1200_examples_donut-2fd6ef62bc12fd93/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5892405e3ae344d2be1f51fa43327dc4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/122M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8b246900f75b4d45a536654ff604f05a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/25.2M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "04c7cdc8260147dcb95d668c970c88a7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting data files:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7cfe19d622bc42a0939bc4aad3d9788f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0 tables [00:00, ? tables/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "777295bf47894151a0fcc1aca9b3b64f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0 tables [00:00, ? tables/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f08637bb5136464c8f075f6a1dde337e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset parquet downloaded and prepared to /root/.cache/huggingface/datasets/nielsr___parquet/nielsr--docvqa_1200_examples_donut-2fd6ef62bc12fd93/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "output_type": "display_data",
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            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "\n",
        "def add_ground_truth(examples):\n",
        "  images = examples['image']\n",
        "  queries = [query['en'] for query in examples['query']]\n",
        "  answers = examples['answers']\n",
        "\n",
        "  ground_truths = []\n",
        "  for image, query, answers in zip(images, queries, answers):\n",
        "    # we need to escape \" characters appearing in the query and/or answer\n",
        "    query = query.replace(\"\\\\\", \"\") # this was just one corrupt example (index 91 of training set)\n",
        "    query = re.sub(' +', ' ', query)\n",
        "    query = query.replace('\"', '\\\\\"')\n",
        "    # let's create the ground truth string\n",
        "    ground_truth_example = '{\"gt_parses\": ['\n",
        "    for idx, answer in enumerate(answers):\n",
        "      answer = answer.replace('\"', '\\\\\"')\n",
        "      ground_truth_example += '{\"question\" : \"' + query + '\", \"answer\" : \"' + answer + '\"}'\n",
        "      # add comma\n",
        "      if idx != len(answers) - 1:\n",
        "        ground_truth_example += ', '\n",
        "    ground_truth_example += ']}'\n",
        "    ground_truths.append(ground_truth_example)\n",
        "  \n",
        "  examples['ground_truth'] = ground_truths\n",
        "  \n",
        "  return examples\n",
        "\n",
        "toy_dataset = toy_dataset.map(add_ground_truth, batched=True)"
      ],
      "metadata": {
        "id": "840aJ4IgkbCK",
        "colab": {
          "base_uri": "https://localhost:8080/",
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          ]
        },
        "outputId": "471237ca-57e9-4a5f-d5d9-a0c358ce5b13"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?ba/s]"
            ],
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        },
        {
          "output_type": "display_data",
          "data": {
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              "  0%|          | 0/1 [00:00<?, ?ba/s]"
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "It's important to verify that this added JSON can be properly read:"
      ],
      "metadata": {
        "id": "x_iDsvY4el4-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "\n",
        "example = toy_dataset['train'][135]\n",
        "json.loads(example['ground_truth'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mDZF17rb02h6",
        "outputId": "db48b949-f91d-47ea-db34-b4597e4e76fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'gt_parses': [{'answer': 'phosphoric acid',\n",
              "   'question': 'Which acid do the coca-cola company use to provide a pleasant tartness?'}]}"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "example = toy_dataset['train'][58]\n",
        "json.loads(example['ground_truth'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R_BARHBqo7v3",
        "outputId": "f0436731-346f-4224-cef9-0d167bf697b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'gt_parses': [{'answer': 'J. T. WINEBRENNER',\n",
              "   'question': 'What is the name of the person in the \"FROM\" field?'}]}"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "example = toy_dataset['train'][91]\n",
        "json.loads(example['ground_truth'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vq_PkMi435X0",
        "outputId": "633efdf9-5cea-4805-cde9-b0099b7ec3e4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'gt_parses': [{'answer': '1995-2000',\n",
              "   'question': 'Between which years estimated growth rates have been taken? ?'}]}"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Push to the hub\n",
        "\n",
        "Finally, we push this dataset to the hub such that we easily reuse it later on."
      ],
      "metadata": {
        "id": "eiq8CN5kskZI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "toy_dataset.push_to_hub(\"nielsr/docvqa_1200_examples_donut\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280,
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        {
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          "text": [
            "WARNING:datasets.dataset_dict:Pushing split train to the Hub.\n"
          ]
        },
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              "Pushing dataset shards to the dataset hub:   0%|          | 0/1 [00:00<?, ?it/s]"
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        },
        {
          "output_type": "stream",
          "name": "stderr",
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            "/usr/local/lib/python3.7/dist-packages/huggingface_hub/hf_api.py:1951: FutureWarning: `identical_ok` has no effect and is deprecated. It will be removed in 0.11.0.\n",
            "  FutureWarning,\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Deleting unused files from dataset repository:   0%|          | 0/1 [00:00<?, ?it/s]"
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          "text": [
            "WARNING:datasets.dataset_dict:Pushing split test to the Hub.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "Pushing dataset shards to the dataset hub:   0%|          | 0/1 [00:00<?, ?it/s]"
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              "Deleting unused files from dataset repository:   0%|          | 0/1 [00:00<?, ?it/s]"
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      },
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      "outputs": []
    }
  ]
}